BOLeRo: Behavior optimization and learning for robots. (10th June 2020)

• Record Type: Journal Article
• Authors: Fabisch, Alexander; Langosz, Malte; Kirchner, Frank
• Is Part Of: International journal of advanced robotic systems. Volume 17:Number 3(2020:May/Jun.)
• Publication Date: 2020-06-10
• Subjects: Reinforcement learning -- evolutionary algorithms -- robotics -- black-box optimization; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881420913741
• Languages: English
• ISSNs: 1729-8806

Abstract

Reinforcement learning and behavior optimization are becoming more and more popular in the field of robotics because algorithms are mature enough to tackle real problems in this domain. Robust implementations of state-of-the-art algorithms are often not publicly available though, and experiments are hardly reproducible because open-source implementations are often not available or are still in a stage of research code. Consequently, often it is infeasible to deploy these algorithms on robotic systems. BOLeRo closes this gap for policy search and evolutionary algorithms by delivering open-source implementations of behavior learning algorithms for robots. It is easy to integrate in robotic middlewares and it can be used to compare methods and develop prototypes in simulation.